Book Review of Everything is Obvious: How Common Sense Fails Us by Dr. Duncan J. Watts

This Book Review of Everything is Obvious: How Common Sense Fails Us by Dr. Duncan J. Watts is brought to you from Ming Liu from the Titans of Investing.

Genre: Business Planning & Forecasting
Author: Dr. Duncan J. Watts
Title: Everything is Obvious: How Common Sense Fails Us (Buy the Book)


What we often refer to as common sense is both a blessing and a curse. It is a necessary trait for us to function in everyday life; you would be quite insulted if someone said you lacked common sense. However, what is necessary to survive on a day-to-day basis is also impairing our ability to make important strategic decisions. Dr. Duncan J. Watts sees common sense as a subconscious figment of our psyche. Although it plays a significant role in our decision-making process, we are often unaware of how it works.

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In this brief, you will find numerous psychological quirks that Dr. Watts has pulled from centuries of psychological and sociological research. Examples of phenomenon such as anchoring and confirmation bias give insights into how our mind functions and how random factors can negatively influence our ability to make effective decisions. Watts also covers other common issues we face such as intuitively falling into post-hoc fallacy and misjudging lessons from history.

By analyzing these factors, Watts advises the pursuit of “uncommon sense.” These methods focus on bringing a scientific and mathematical approach to traditionally intuitive problems. Ideas such as harnessing probability, something financial gurus may know as the “wisdom of crowds,” can be used to improve predictive success.

Formulating multiple strategies allows us to address the variability that inevitably occurs within our world. Certain areas such as the retail industry may even benefit from the usage of measure-and-react strategies as evidenced by successful Spanish clothing retailer, Zara.

Regardless of the strategy, Watts ultimately suggests adopting a mindset of skepticism. Although advances in technology have raised our social science effectiveness to historical highs, our human subconscious will still continue to hamper our decision-making. Only through constant vigilance and understanding of our common sense quirks are we able to conquer our modern-day problems.


“Although my training had been in physics and mathematics, my interests had turned increasingly toward the social sciences and I was just beginning what turned out to be a career sociology.” – Duncan J. Watts

Author Duncan J. Watts earned an undergraduate degree in physics and completed his doctorate in theoretical and applied mathematics. Despite his background in the hard sciences, Watts ultimately transitioned to the field of sociology due to his interest in social networks.

At the time, many of his physicist friends looked down upon the field, a sentiment encapsulated by physicist John Gribbin who cited the field as “something of an oxymoron,” whose insights “real scientists learn in the cradle.”

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Unfazed by his colleagues’ critiques, Watts seeks to validate the field of sociology by combining his own scientific findings with other insights in order to provide us with a new way of thinking. Watts often responds to skeptics by citing fellow sociologist Paul Lazarsfeld’s work on The American Soldier, a then-recently published report on the lives of more than 600,000 servicemen shortly after WWII.

Lazarsfeld listed six key findings from the study that he claimed represented the report.

For example, one finding stated, “Men from rural backgrounds were usually in better spirits during their Army life than soldiers from city backgrounds.” Readers who observed this response were quick to confirm this conclusion, perhaps claiming aloud:

“Rural men were more acclimated to the harsher living standards and physical labor than city men, so they naturally had an easier time adjusting!”

To some, this reasoning was so obvious they criticized the study as a large waste of time and money. However, Lazarsfeld surprised readers when he announces at the end of his report that all six of his findings were in fact the exact opposite of what he initially listed. Upon seeing this, many readers jumped to reconcile the new facts with some other form of reasoning. Those who earlier validated the rural soldier’s tenacity were now singing a different tune:

“City men are more used to working in crowded conditions and in corporations, with chains of command, strict standards of clothing, and social etiquette, and so on. That’s obvious!”

From this example, the point that Lazarsfeld was trying to make is clear – when every answer and its opposite appear equally obvious, then there must be something wrong with the argument of obviousness.

In his book, Watts presents a multitude of psychological biases that undermine our common sense and constantly questions our understanding of “obviousness.” By clarifying some of the flaws in common sense thinking, he is then able to identify some “uncommon sense” methods that can help to strengthen our decision-making processes.


“The Paradox of common sense… is that even as it helps us make sense of the world, it can actively undermine our ability to understand it.” – Duncan J. Watts

The concept of common sense is difficult to pin down. At best, people agree that it is a combination of generalized and highly specialized knowledge regarding customs and behaviors. Deceivingly simple, many people feel that it requires no explanation – after all, it is just common sense. However, common sense is not so common at all; its attributes vary widely between different social groups and cultures.

The Ultimatum Game

Each round of the ultimatum game requires two participants. The first participant is given $100 and a chance to offer the other player a certain portion of the $100. The second player can either accept or deny the offering. Should the second player choose to deny the offering, both players receive nothing.

When conducted in industrialized societies, researchers demonstrated that most players propose a 50-50 split and offers under $30 are typically rejected. Most economists would find any sort of rejection quite disturbing. Rational theory explains that the second player sacrifices something for nothing by rejecting the offer and thus all offers above $0 should be accepted.

However, some economists point out that the second player maximizes his utility by teaching the first player a lesson on fairness.

Both seem like good explanations that cover the spectrum of humanity that may play the game. However, when replicated in small-scale preindustrial societies, researchers found people in different societies defined fair very differently.

At one extreme, the Machiguenga tribe of Peru offered only about a quarter of the total amount but saw virtually no rejections. On the other extreme, the Au and Gnau tribes of Papua New Guinea made offers above the 50-50 split but often saw their offers rejected in large numbers.

Although confusing at first, researchers learned that differences in the interpretation of gift- exchange largely determined how participants played the game. The Au and Gnau tribes had long-established customs of gift exchange which required any gift to be reciprocated at some point in the future.

Meanwhile, the Machiguenga society only expected bonds between immediate family members to remain loyal. The take away is that common sense is only common when two people share sufficiently social and cultural experiences. For those who lack such understanding, even conflicts over “common sense” can be surprisingly difficult to resolve.

The Misuse of Common Sense

Although a large part of our routine life, the highly practical application of common sense is incredibly ineffective at dealing with complex social phenomena. Ironically, when large disasters occur we often attribute the problem to failures in common sense. For example, at an early 2009 World Economic Forum meeting in Davos, one audience member announced to the audience,

“What we need now is a return to common sense!”

His outcry drew large applause from the crowd. Yet further retrospection would find the same mix of businesspeople, politicians, and economists, who attended the same annul Davos meeting just two years earlier, had congratulated one another for generating astonishing levels of wealth and stability in the financial sector. In reality, the issue was not a shortage of common sense but an over-reliance upon an all-too-human tool ill-equipped to handle the situations at hand.

How Common Sense Fails Us

We break down the deficiencies of common sense into three main parts.

First, we focus on factors like incentives, motivations, and beliefs when we think about why people do what they do. Although these factors are important, they are not comprehensive in understanding how decisions are made. Decades of psychological research proves that seemingly irrelevant factors, such as background music or font size, can have a large impact on how we make choices.

Second, while our mental model for individual behavior is systematically flawed, our model for collective behavior is even worse. When examining large collective groups, we are unable to understand all the component parts and how they combine to create new and unexpected behaviors. As a result, we often instinctively fall back to the logic of individual actions, utilizing “representative individuals” to bridge the gap.

The third problem is that we learn less from history than we think we do. This inflates our confidence in predicting the future and thus skews our ability to predict effectively.

We are instinctively drawn to look for explanations whenever dramatic events occur. Unfortunately, we are often quick to piece the past together and over-emphasize what actually happened relative to what might have transpired.


“Economics is all about choice, while sociology is about why people have no choices.” – James Duesenberry

In order to understand how to make better “uncommon sense” decisions, we must first understand ourselves. Many subconscious factors drive us to behave in certain ways; through social sciences such as psychology and sociology, we have identified some of these factors.

Rational Behavior Framework When we think about how we think, we reflexively adopt a framework of rational behavior based on our own interpretation of rational. For example, when we try to understand why an ordinary Iraqi citizen would wake up one morning and decide to turn himself into a living bomb, we implicitly rationalize his behavior.

Although this habit of rationalizing every situation is noble, we must remain skeptical about our understanding of human behavior. Consider for a moment how we choose to understand the behaviors of others. Most of us would put ourselves in their shoes and pretend as if we were them.

In a hard science such as physics, this would be comparable to pretending to be an electron – a rather outlandish notion. However, we do this so often with other people that it has become instinctive; leaving us susceptible to overlooking vital details that may affect our interpretation.

Priming In the field of psychology, many experiments have shown that individual choices and behavior can be influenced simply by “priming” them with particular words, sounds, or other stimuli. For example, subjects walk more slowly down the lab corridor while leaving if they read the words “old” and “frail” during the experiment. Survey respondents asked about energy drinks are more likely to list Gatorade if provided with a green pen.

Anchoring Psychologists have also discovered a strong anchoring effect that we use to evaluate ambiguous scenarios.

Anchoring is the usage of a specific number or example that will subconsciously serve as a standard for future decision-making. For example, scientists asked participants at a wine auction to write down the last two digits of their social security numbers before bidding. Although these numbers were essentially random, buyers who had to write down higher numbers were willing to bid more than those who wrote down lower numbers.

Context The context in which information is presented also has a large impact on how we make decisions. For example, most of us have a strong and almost irrational aversion to loss. We can observe this phenomenon in the equity markets where, despite the prevalence of bull markets on positive returns, many investors are still permanently scared off when approached by a smaller bear market.

An even more puzzling phenomenon regarding context is how introducing a third alternative influences an individual’s preferences between two items. Let us assume a 40” plasma T.V. sells for $300 and a 32” LED T.V. sells for $200. Assuming they did not have a strong preference for a specific size, many participants would have difficulty choosing which T.V. to purchase.

However, if you introduce a third option – say a 28” LED for $220, almost all participants would choose the original 32” LED T.V. By introducing the third option, your evaluation of the two previously distinct choices now has a frame of reference. Although it was hard to compare the 40” plasma to the 32” LED on a price-performance metric, the 32” LED option is definitely better than the 28” LED option.

Confirmation Bias Even when interpreting same sets of information, people can arrive at incredibly different conclusions depending on their pre-existing beliefs. The Israeli-Palestinian conflict demonstrates how two widely differing opinions often stem from the same set of facts. Physicist Max Planck famously acknowledged this phenomenon, stating:

“A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die.”

The Unfortunate Truth

With a long list of quirks describing our decision-making process, it has become evident that our ability to make choices is much more complex than we make it out to be. When we imagine ourselves making decisions, we do not proceed to make a large list of possible details that factor in phenomenon such as priming, anchoring, context, and confirmation bias.

Instead, we instantly fill in these details and are only able to evaluate the details in hindsight – an issue known as the frame problem.

Aside from these cognitive quirks, we are also influenced by certain ways of thinking which upon examination, are highly illogical and detrimental to our understanding of the world. Examples of these include circular reasoning and the pervasive usage of representative agents.

Circular Reasoning

When explaining the success of the Mona Lisa, experts are quick to point out the intrinsic attributes of the painting itself – the enigmatic smile, the combination of light, the gauzy finish. However, many of these qualities are not inherently visible upon first seeing the Mona Lisa.

In fact, the painting will most likely disappoint visitors when they first see it due to its rather underwhelming presence. What is it then that has driven the Mona Lisa to become, arguably, the most famous painting in existence?

Supporters would say that the Mona Lisa’s combination of intrinsic attributes makes it the best painting in the world.

Unfortunately, this argument counteracts itself because the criteria for success is determined based on first deeming the Mona Lisa as the best. Although we pretend to say that the Mona Lisa is successful because of having X, Y, and Z, we are actually saying that the Mona Lisa is famous because it is more like the Mona Lisa than any other painting.

Micro-Macro Problem

The circularity in common sense explanations is noteworthy because it comes from one of the central intellectual problems of sociology – the micro-macro problem. This problem exists because while sociologists are trying to solve issues that are intrinsically “macro” in nature, they can only be observed through the “micro” actions of individuals.

Other sciences have traditionally avoided this problem by dodging the question and utilizing distinctive sets of facts, laws, and regularities for each science; biology operates on a different set of rules from chemistry, which operates on a different set than physics.

Unfortunately, most of the pressing questions coming from both social and hard sciences involve examination across scales. We want to examine whole gene interactions and identify how individual expressions work to create new, combined effects. We want to understand how interactions between individuals and countries work to create markets and global politics.

Representative Agents

Unfortunately, our common sense has a knack for ignoring this micro-macro problem and the issues associated with emergence. Instead, we use representative agents such as “The Market” or “Target Demographics” to simplify the situation. Famous concepts such as Malcolm Gladwell’s “Influencers” essentially place large amounts of power into a few actors.

This simplification ignores phenomenon such as the “Matthew effect,” an observations that advantages build upon themselves to garner even more advantages. This concept is similar to that of the “butterfly effect” and explains how small-scale drivers that are often insignificant and largely ignored can determine the outcome of an entire situation.


“For problems of economics, politics, and culture… the combination of the frame problem and micro-macro problem means that every situation is in some important respect different from the situations we have seen before… Nevertheless, we do still expect to learn some lessons from history, and it is all too easy to persuade ourselves that we have learned more than we really have.” – Duncan J. Watts

A central theme so far to this brief is the need to remain skeptical of ourselves. By being cognizant of the flaws within our common sense mode of thinking, we shield ourselves from the consequences of our hubris. Other methods to protect ourselves include the studying of history, yet even then we have to remember how common sense may misconstrue our interpretation of the past

Misrepresented Cause and Effect

A common flaw with studying history is the idea of “Creeping Determinism,” or the tendency to pay too little attention to things that do not happen and too much to things that have. For example, we notice when a mutual fund manager beats the S&P 500 10 years in a row, but largely ignore all the times when they do not. Although it makes sense we would focus primarily on the interesting things, it exacerbates our tendency to construct explanations that only account for fragments of data.

There is also a tendency to ignore the sampling bias associated with historical events.

Many major events in history such as wars or stock market crashes occur only sporadically. Assigning statistical significance to such rare occurrences is dangerous. Creeping determinism further amplifies the sampling bias since we may place an even stronger emphasis on these historical events.

The combination of creeping determinism and sampling bias leads to a side effect known as post-hoc fallacy. In simple terms, post-hoc fallacy is a fundamental misunderstanding that since A precedes B, then A causes B. Consider Gladwell’s concepts of the “Law of the Few” in his book The Tipping Point.

One of his stories emphasizes the connective capabilities of Paul Revere and how it empowered him to successfully rally the minutemen while William Dawes, another horseman attempting to alert the local militia the same night as Paul Revere’s ride, only met failure.

Although it made for a great story, Gladwell forgets that there were fundamental differences about the two rides: different routes, different towns, and different people who made different choices about whom they wanted to alert after hearing the news from the messengers. Just because Revere was a connector and successful in his task does not mean that being a connector is what made Revere’s successful that night.

Power of Storytelling

Historical explanations, in other words, are not always causal explanations. Rather, historian John Lewis Gaddis describes them as stories constrained by certain historical facts and pieces of observable evidence. As with any good story, the historical explanations concentrate on interesting portions while downplaying other factors that may have influenced the actual outcome.

This act of simplifying and generalizing leaves us with constructs that seem similar to the sorts of theories that exist in science – acting as a connector leads to success and gravity leads to the falling of an apple. Making the switch between telling stories and constructing theories comes so naturally to us that we almost forget the fundamental differences between these story-based theories and methodically honed theories from science.

Faulty Predictions

This tendency to view the past as deterministic can greatly distort our ability to predict the future. Steven Schnarrs, a management scientist, analyzed hundreds of predictions made in the 1970’s regarding technology-trends and found that nearly 80% of them were wrong with no distinction between those made by experts and those who were not.

However, the problem is not that we are universally good or bad at predictions, but rather that we are bad at distinguishing predictions we can make from ones that we cannot. Our ability to excel in predicting certain problems has tricked us into approaching other problems in the same way.

Laplace’s Demon

About a century after Newton proposed his theories of motion and gravity, a French mathematician and astronomer by the name of Pierre-Simon Laplace proposed a rather grand idea. Laplace envisioned an “intellect” which understood all the forces of natures and could predict the future.

Now known as Laplace’s Demon, the idea views the world as a “clockwork universe” capable of being perfectly charted out. Although this demon has since lost its charm due to discoveries in the field of quantum mechanics and chaos theory, the notion that we can somehow reach a point where the future is easily predictable continues to entrance our minds.

Simple and Complex Problems

The fundamental flaw in Laplace’s vision was that he assumed all problems were identical. In reality there are two fundamental types of problems, which we can designate as simple and complex. While simple problems are linear, complex problems are composed of interdependent components interacting in nonlinear ways. We cannot attempt treat these as simple math problems and attempts to do so will be met with failure.


“Just because we can’t make the kinds of predictions we’d like to make doesn’t mean that we can’t predict anything at all.” – Duncan J. Watts

Although our intuition often hampers our predictive abilities, we can still find ways to improve the accuracy of our predictions.

Harnessing Probability

In situations where probability can be utilized, predictions can quite often be accurate. The financial sector often refers to this concept as the “wisdom of crowds.” Such notion acknowledges that although individuals are likely to make error-prone predictions, the aggregate of these predictions is fairly accurate. Prediction markets such as the Iowa Electronics Market and Hollywood Stock Exchange exist and serve as aggregators of public opinion.

For situations where quantitative analysis through a market-exchange may not be as possible, we can still learn from individual data points. The key lies in the dedication to gaining as many expert opinions as possible, preferably from different sources with different perspectives.

Future Shocks

As with any strategy, harnessing probability still has a major flaw if predictions are based off historical data. For some situations – predicting NFL playoff records or MLB batting averages – historical data may suffice. For others, such as the mortgage crisis of 2008, collaterized debt obligation (CDO) models utilizing historical data were completely inept in predicting the upcoming crisis.

As mentioned earlier, certain strategic initiatives only arise rarely and the pool of scenarios to draw from is often slim. In such cases, value from predictions may come less from the certainty of the prediction and more from considering the prediction itself.

Strategic Flexibility

Strategic Flexibility is a form of scenario planning that acknowledges the limitations to prediction. It seeks to sketch out a wide range of possibilities instead of focusing on a single scenario. By planning multiple scenarios, each with different factors and assumptions, one can hedge against uncertainty.

Unfortunately, maintaining strategic flexibility is a time-consuming process that can also provide a sense of false security. Indeed, it is possible to plan for multiple scenarios, but what if an event not accounted for occurs? In this situation, strategic flexibility may actually serve as a hindrance if the company is over-confident and as a result, dangerously caught off guard.

Measure and React

For companies operating in fashion retail, it is a costly and risky endeavor to try to predict upcoming industry trends. As a result, Spanish company Zara has taken an alternative approach by focusing less on predicting fashion movements and instead developing a measure-and-react strategy. Instead of coming up with designs before every season, Zara observes what the markets desire and work quickly to create products to fit the styles being demanded.

Management theorist Henry Mintzberg anticipated this measure-and-react approach in a concept that he called “emergent strategy.” Rather than attempting to anticipate what will work in the future, he recommends you improve the ability to adapt to what is working right now.

Utilizing Experiments and Technology

In many circumstances, however, measuring trends alone may be insufficient. Observing correlation does not provide knowledge on causation. It is important to identify causal factors in order to enhance prediction abilities.

For example, most CEO’s agree that marketing is vital to driving sales but are incapable of determining actual impact. Effective experiments that utilize control groups, localized knowledge, and emergent technologies help address these issues. Control groups represent the standard upon which experimental variables are compared.

Localized knowledge represents the usage of market-based mechanisms to harness the creative power of communities. Emergent technologies include the internet and creation of “big data” systems capable of crowdsourcing surveys and experiments. In tandem, these strategies allow corporations and governments to achieve a level of planning far surpassing the capabilities of those before them.


“Why should the science required to understand social problems such as urban poverty or economic development or public education deserve less attention?” – Duncan J. Watts

Some observers currently compare sociology to physics and notice a disparity in progress. While physics has blossomed with an expansion of universal laws, sociology remains a basket case of “special” scenarios.

Two decades ago, sociologist Robert Merton proposed that his colleagues focus on “theories of the middle range” instead of those of universal grandeur. Perhaps Merton was right, but with the development of recent technologies, there may be a new alternative in sight.

New, large-scale social experimentation driven by the internet has enabled us to perform experiments that were impossible until the 21st century. Through findings from the newly empowered social sciences, we can truly begin to understand how to navigate around the flaws of our common sense. would like to thank the Titans of Investing for allowing us to publish this content.  Titans is a student organization founded by Britt Harris. Learn more about the organization and the man behind it by clicking either of these links.

Britt always taught us Titans that Wisdom is Cheap, and principal can find treasure troves of the good stuff in books.  We hope only will also express their thanks to the Titans if the book review brought wisdom into their lives.

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